The 2016 election cycle has been one of the most unusual in American history. In this election, the politicians experienced with running states and nations are often floundering while those with little experience are promising a new brand of leadership. The 2016 election is also marked by debates over fundraising. It is the first presidential election since the Supreme Court Citizen’s United decision where campaigns have been fully taking advantage of Super PAC funding. Bernie Sanders boasts often of his low average donations; Donald Trump was for a while self-funding his campaign. In this light, I will investigate direct donations to all candidates, failed or still in the running, and examine the effects of various variables on fundraising.
## cmte_id cand_id cand_nm
## Length:56764 P00003392:15980 Clinton, Hillary Rodham :15980
## Class :character P60007168:14620 Sanders, Bernard :14620
## Mode :character P60005915: 7826 Carson, Benjamin S. : 7826
## P60006111: 6675 Cruz, Rafael Edward 'Ted': 6675
## P60006723: 3102 Rubio, Marco : 3102
## P60007242: 2299 Fiorina, Carly : 2299
## (Other) : 6262 (Other) : 6262
## contbr_nm contbr_city contbr_st
## IMEOKPARIA, OSI : 52 LOS ANGELES : 4082 CA:56764
## BATTS, ERIC : 43 SAN FRANCISCO: 3747
## BUCCHERE, CHRIS : 41 SAN DIEGO : 1887
## HANNON, STEPHANIE : 40 SAN JOSE : 1167
## PAULISSIAN, MARTHA: 40 OAKLAND : 921
## OFTEDAL, EGIL MR. : 37 SACRAMENTO : 775
## (Other) :56511 (Other) :44185
## contbr_zip contbr_employer contbr_occupation
## 920274404: 56 RETIRED :10028 RETIRED :12059
## 943061338: 52 NOT EMPLOYED : 5473 NOT EMPLOYED : 4713
## 913565823: 46 SELF-EMPLOYED: 4784 ATTORNEY : 1947
## 958256321: 44 N/A : 3035 HOMEMAKER : 1436
## 941311708: 43 SELF EMPLOYED: 2129 INFORMATION REQUESTED: 1217
## 949601336: 41 (Other) :31233 (Other) :35380
## (Other) :56482 NA's : 82 NA's : 12
## contb_receipt_amt contb_receipt_dt receipt_desc
## Min. :-10000.0 30-SEP-15: 3279 :55408
## 1st Qu.: 35.0 30-JUN-15: 1665 Refund : 349
## Median : 100.0 29-SEP-15: 1305 REDESIGNATION TO GENERAL : 258
## Mean : 474.1 23-SEP-15: 982 REDESIGNATION FROM PRIMARY: 254
## 3rd Qu.: 250.0 15-SEP-15: 924 REATTRIBUTION FROM SPOUSE : 94
## Max. : 10800.0 28-SEP-15: 878 REATTRIBUTION TO SPOUSE : 94
## (Other) :47731 (Other) : 307
## memo_cd memo_text form_tp
## :55539 :42982 SA17A:56161
## X: 1225 * EARMARKED CONTRIBUTION: SEE BELOW:12193 SA18 : 254
## EARMARKED FROM MAKE DC LISTEN : 387 SB28A: 349
## REDESIGNATION TO GENERAL : 258
## REDESIGNATION FROM PRIMARY : 254
## REATTRIBUTION FROM SPOUSE : 94
## (Other) : 596
## file_num tran_id election_tp
## Min. :1003942 SA17.300674: 3 : 49
## 1st Qu.:1024052 SA17.365962: 3 G2016: 951
## Median :1029414 C1013462 : 2 O2016: 281
## Mean :1026768 C1015104 : 2 P2016:55482
## 3rd Qu.:1029462 C1015363 : 2 P2018: 1
## Max. :1029674 C1015437 : 2
## (Other) :56750
## 'data.frame': 56764 obs. of 18 variables:
## $ cmte_id : chr "C00577130" "C00577130" "C00577130" "C00577130" ...
## $ cand_id : Factor w/ 21 levels "P00003392","P20002721",..: 10 10 10 10 10 10 10 10 10 9 ...
## $ cand_nm : Factor w/ 21 levels "Bush, Jeb","Carson, Benjamin S.",..: 17 17 17 17 17 17 17 17 17 16 ...
## $ contbr_nm : Factor w/ 24895 levels "A DOSS, CANDALON",..: 5436 8076 8120 8262 16036 16049 16113 10769 10799 19940 ...
## $ contbr_city : Factor w/ 1022 levels "","29 PALMS",..: 794 39 174 791 613 110 38 791 828 505 ...
## $ contbr_st : Factor w/ 1 level "CA": 1 1 1 1 1 1 1 1 1 1 ...
## $ contbr_zip : Factor w/ 21215 levels "","00000","11205",..: 14807 19762 5938 7509 16599 5657 4141 7356 18357 2567 ...
## $ contbr_employer : Factor w/ 9407 levels ""," APPLE INC.",..: 206 5791 5791 7323 5791 5791 5791 8753 5791 2816 ...
## $ contbr_occupation: Factor w/ 4464 levels "",".COM EXECUTIVE",..: 3842 2615 2615 3601 2615 2615 3427 2489 2615 3294 ...
## $ contb_receipt_amt: num 50 100 1000 5 196 ...
## $ contb_receipt_dt : Factor w/ 274 levels "01-APR-15","01-AUG-15",..: 93 170 170 170 189 170 170 52 52 215 ...
## $ receipt_desc : Factor w/ 22 levels "","2016 SENATE PRIMARY DONOR REDESIGNATION FROM PRIMARY",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ memo_cd : Factor w/ 2 levels "","X": 1 1 1 1 1 1 1 1 1 1 ...
## $ memo_text : Factor w/ 88 levels "","*","* EARMARKED CONTRIBUTION: SEE BELOW",..: 3 3 3 3 1 3 3 3 3 1 ...
## $ form_tp : Factor w/ 3 levels "SA17A","SA18",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ file_num : int 1029414 1029414 1029414 1029414 1029414 1029414 1029414 1029414 1029414 1029436 ...
## $ tran_id : Factor w/ 56511 levels "A000771210424405B8CF",..: 47278 48223 48147 48200 50425 48204 48202 46887 46898 37882 ...
## $ election_tp : Factor w/ 5 levels "","G2016","O2016",..: 4 4 4 4 4 4 4 4 4 4 ...
## [1] "Bush, Jeb" "Carson, Benjamin S."
## [3] "Christie, Christopher J." "Clinton, Hillary Rodham"
## [5] "Cruz, Rafael Edward 'Ted'" "Fiorina, Carly"
## [7] "Graham, Lindsey O." "Huckabee, Mike"
## [9] "Jindal, Bobby" "Kasich, John R."
## [11] "Lessig, Lawrence" "O'Malley, Martin Joseph"
## [13] "Pataki, George E." "Paul, Rand"
## [15] "Perry, James R. (Rick)" "Rubio, Marco"
## [17] "Sanders, Bernard" "Santorum, Richard J."
## [19] "Trump, Donald J." "Walker, Scott"
## [21] "Webb, James Henry Jr."
The election is currently fairly crowded with 21 candidates in both parties. By number of donors, though, most of them aren’t competing at all:
The first thing that I noticed about the graph was that the names were too long to fit nicely onto the graph. I decided to cut off first names to simply to the last name of the candidate so that names are more easily visible in graphs:
Clearly, the two major Democratic candidates (Sanders and Clinton) are leading by a long shot in terms of number of contributions. This makes sense, as California is a heavily Democratic state. I decided to add political party information into the data.
After adding political party affiliation, I decided to also include the current polling numbers (in California) of the candidates. This represents the percentage of voters belonging of the candidates’ respective parties who say they would vote for the candidate.
Of course, it isn’t a good idea to compare the polling of candidates of different parties, as they aren’t polling against each other at this point. Some candidates, although they have received donations have 0 support in polls, although this is often because they recently dropped out.
I then decided to create a scatterplot matrix to see what I should investigate next.
Because of the absense of much quantitative data, the scatterplot matrix did not prove particularly insightful. However, I was intrigued by the monetary value of donations, as well as the different cities and towns contributing. First, I decided to investigate the monetary value of donations.
I realized that there were some contributions which were negative, usually because they were “Redesignations”, “Reattributions” or “Refunds”. They were usually paired with another value with the same description that was positive with the same absolute value. I removed these for the sake of graphing. When I did, I noticed that the data was highly skewed, with most donations at less than $1000 dollars but some at almost $3000. I applied a log ten scale and realized that (not surprisingly) people tend to give donations at regular amounts.
I immediately noticed that although Sanders is close to Clinton in terms of number of donations, he isn’t in terms of amount raised.
Looking at these two graphs side by side, Cruz and Carson seem to have dropped relatively as well. I decided to compare these two variables (number of donations vs total donation size).
Generally, there is a clear correlation between number of donors and total donation amount. However, some candidates raise more money than would be expected by the number of donations, others less. An interesting observation that I made was that candidates lying above the trendline on this graph are generally considered “establishment” candidates and have held high-level offices in government, generally having large amounts of experience. Those lying below include some establishment candidates but also three “upstart” senators (Paul, Cruz, and Sanders) and three candidates who have never held elected office (Trump, Lessig, and Carson). This seems to hold true for both parties. This observation makes a good amount of sense: establishment candidates tend to have the better-funded experienced political forces donating to their campaigns, while candidates attempting to disrupt will be less likely to. Another observation that I made was that candidates below the margin of error seemed to be doing better in polls than those above on average (except Clinton). I made another graph to investigate:
It would seem that this election, Republicans with higher average donations generally have lower support in polls. Of course, there is no way to know given this data why this is, but I hypothesize that it may have something to do with the anti-establishment fervor this election. This trend does not seem to hold true for Democrats, although of course there are only three datapoints.
##
## Pearson's product-moment correlation
##
## data: subset(summarised, prty = "R")$mean and subset(summarised, prty = "R")$polls
## t = -1.9287, df = 19, p-value = 0.06885
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.71195723 0.03278233
## sample estimates:
## cor
## -0.4046307
The correlation appears to be negative as noted, though it is not statistically significant. Next, I decided to look at how much each candidate raised per percent support in the polls.
Clearly, Bush is raising a huge amount of money for the small support he has in the polls. Clinton is also raising proportionally higher numbers. Sanders, Carson, and Christie are all raising lower amounts for their support. And Donald Trump is also raising almost nothing for his high support. Next I turned my attention to regional differences.